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Machine learning overcomes human bias in the discovery of self-assembling peptides


Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow—AI-expert—that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

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Fig. 1: Workflow adopted to discover self-assembling pentapeptides using inputs from human experts and the developed AI-expert.
Fig. 2: Performance comparison of the different search strategies for the space of tripeptides.
Fig. 3: Screening of pentapeptides from AI-expert and human experts.
Fig. 4: Experimental measurements of self-assembly in pentapeptides.
Fig. 5: Performance comparison of AI-expert and human experts.

Data availability

The data that support the findings of this study are available in the Extended Data figures (for synthesized pentapeptides), the Supplementary Information (for AI-expert-proposed pentapeptides) and the accompanying code repository at (for tripeptides). Source data are provided with this paper.

Code availability

The codes underlying the AI-expert framework are freely available for general use under a Creative Commons Attribution 4.0 International license and are deposited at


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Work performed at the Center for Nanoscale Materials, a US Department of Energy (DOE) Office of Science User Facility, was supported by the US DOE, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357, and additionally supported by the University of Chicago and the DOE under DOE contract no. DE-AC02-06CH11357 awarded to UChicago Argonne, LLC, operator of the Argonne National Laboratory. This material is based on work supported by the DOE, Office of Science, BES Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities programme (Digital Twins). We gratefully acknowledge the computing resources provided on Bebop, the high-performance computing clusters operated by the Laboratory Computing Resource Center (LCRC) at Argonne National Laboratory. S.K.R.S.S. acknowledges support from the UIC faculty start-up fund. We acknowledge T. Tuttle for sharing computational data on tripeptides.

Author information

Authors and Affiliations



R.B., S.K.R.S.S. and H.C.F. designed the study. R.B., T.D.L., H. Chan, S.S. and S.K.R.S.S. designed the AI-expert algorithm. R.B. organized and analysed the AI-expert output. H. Cui, I.V.K., V.N., L.C.P., L.A.S. and H.C.F. designed the human-expert peptides. H.C.F. performed the experimental work (peptide synthesis, LCMS, UV–vis plate reader and FTIR). R.B., S.K.R.S.S. and H.C.F. wrote the manuscript.

Corresponding authors

Correspondence to H. Christopher Fry or Subramanian K. R. S. Sankaranarayanan.

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The authors declare no competing interests.

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Nature Chemistry thanks Shuguang Zhang, Jin Kim Montclare, Fabien Plisson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Top scoring tripeptides.

Top ranked tripeptides identified using the brute-force computational search on 8000 candidates. The score is based on the reward function rtri. Abbreviations: AP, aggregation propensity; logP; hydrophobicity.

Source data

Extended Data Fig. 2 Overall results for the synthesized pentapeptides.

Computational (AP, logP) and experimental (LC(RT), OD800nm) measurements, along with the associated reward scores (rpenta, rtri) and experimental score (ExpScore) are provided. β-sheet scale corrected rpenta and rtri scores, respectively titled rpentawB and rtriwB, are also included. Cases where aggregation (Agg.) was observed are marked 1 with a bold font.

Source data

Extended Data Fig. 3 Diversity of pentapeptides proposed by our human experts.

Frequency of occurrence of (left panel) amino acids in the 29 human expert proposed sequences and (right panel) the overall charge distribution of those sequences. It is evident that human experts preferred to include V, K and F amino acids and overall charge neutral pentapeptides sequences. The complete list of the pentapeptides proposed by the human experts and the rationale for choosing/rejecting a sequences for synthesis is provided in Supplementary Information Table S2.

Source data

Extended Data Fig. 4 MCTS hyperparameter study.

Effect of the exploration constant c in Eq. 1 on the search efficiency of AI-expert for the case of tripeptides with (a) just the MCTS scheme and (b) with the MCTS+RF scheme. The boxplots showcase the number of runs needed to find the topmost scoring tripeptide. The minima and maxima bounds of box represent the 25th and 75th percentile, the middle line the median, the upper whiskers extended to last datum less than 75th percentile + 1.5(IQR), lower whiskers extended to first datum greater than 25th percentile - 1.5(IQR), and data beyond the whiskers are plotted as individual points. Here, IQR signify interquartile range given by 75th - 25th percentile. The results are based on n=10 statistically independent runs. Number of trials needed using a brute-force or random search (on average) are also shown using dotted lines. The MCTS+RF scheme performs the best—not only is the MCTS+RF scheme less sensitive to the choice of c parameter, it also finds the topmost scoring tripeptide more efficiently. The MCTS+RF scheme with c = 10 was found to be most efficient and thus was selected for the pentapeptide search.

Extended Data Fig. 5 Machine learning aggregation propensity.

Performance of the random forest (RF) model to predict the computed aggregation propensity (AP) in a) tripeptides and b) pentapeptides. In both cases improvement in the RF model performance with increasing size of training data (left panel) is shown, along with an example parity plot of the test data when it constitutes 20 % of the total dataset. In a) n=10 statistically independent runs with a random split of test-train data (from 8000 total cases) were performed. Here, data are presented as mean values +1.5/-1.5 SD. In b) the test-train split (from ~ 6600 total cases using rpenta) was performed in a special manner to capture the progressive improvement of the RF model during the MCTS run. Since within the MCTS+RF scheme the training data was generated in an online fashion, the RF model training set consists of AP values evaluated in the early stages of the MCTS run while the test set contains AP values evaluated in the later stage of the run. Abbreviation: MAE, mean absolute error; SD, standard deviation.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Discussion and Tables 1–4.

Reporting Summary

Source data

Source Data Figure 3

Source data for AI-proposed ALL pentapeptides, top AI, top human, and synthesized pentapeptides.

Source Data Figure 4

Source data for pentapeptide characterization.

Source Data Figure 5

Source data for pentapeptide characterization with beta-sheet factor.

Source Data Extended Data Figure 1

Source data for top-scoring tripeptides.

Source Data Extended Data Figure 2

Source data for overall results for the synthesized pentapeptides.

Source Data Extended Data Figure 3

Source data for diversity analysis of human expert proposed candidates.

Source Data Extended Data Figure 5

Source data for RF surrogate models of aggregation propensity.

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Batra, R., Loeffler, T.D., Chan, H. et al. Machine learning overcomes human bias in the discovery of self-assembling peptides. Nat. Chem. 14, 1427–1435 (2022).

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